CN108327716B - Collision mitigation and avoidance - Google Patents
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- CN108327716B CN108327716B CN201810031443.5A CN201810031443A CN108327716B CN 108327716 B CN108327716 B CN 108327716B CN 201810031443 A CN201810031443 A CN 201810031443A CN 108327716 B CN108327716 B CN 108327716B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0953—Predicting travel path or likelihood of collision the prediction being responsive to vehicle dynamic parameters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
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- G08G1/056—Detecting movement of traffic to be counted or controlled with provision for distinguishing direction of travel
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/161—Decentralised systems, e.g. inter-vehicle communication
- G08G1/163—Decentralised systems, e.g. inter-vehicle communication involving continuous checking
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
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Abstract
The present disclosure determines a distance offset value between the target and the host vehicle based on the determined time-to-collision, relative lateral distance, and relative longitudinal distance. A threat assessment value is determined based on the distance offset value and the distance threshold value. A component of the host vehicle is actuated based on the threat estimate.
Description
Technical Field
The present invention relates to a method and system for collision mitigation and avoidance.
Background
Vehicle collisions often occur at intersections. Collision mitigation can be difficult and expensive to implement. For example, determining a threat estimate for a target may require data from multiple sensors. Further, collision mitigation techniques that may be used to mitigate rear-end collisions may be different than techniques that may be used to mitigate cross-road collisions.
Disclosure of Invention
According to the present invention, there is provided a system comprising a computer programmed to:
determining a distance offset value between the target vehicle and the host vehicle based on the determined time-to-collision, relative lateral distance, and relative longitudinal distance;
determining a threat estimate based on the distance offset value and the distance threshold; and
a component of the host vehicle is actuated based on the threat estimate.
According to an embodiment of the invention, the computer is further programmed to actuate the brake when the threat assessment value exceeds a threshold value.
According to one embodiment of the invention, wherein the computer is further programmed to determine a lateral time of impact and a longitudinal time of impact.
According to one embodiment of the invention, the computer is further programmed to determine a longitudinal distance offset value based on the lateral time-to-collision and a lateral distance offset value based on the longitudinal time-to-collision.
According to one embodiment of the invention, the computer is further programmed to determine a longitudinal distance threshold based on the lateral time-to-collision and determine a lateral distance threshold based on the longitudinal time-to-collision.
According to one embodiment of the invention, the computer is further programmed to determine a threat estimate based on the lateral time-to-collision and the longitudinal time-to-collision.
According to one embodiment of the invention, wherein the threat estimates are a number of braking threats, the number of braking threats being a measure of the change in acceleration of the host vehicle for allowing the host vehicle to stop or the target vehicle to pass the host vehicle.
According to one embodiment of the invention, wherein the time to collision is based on an acceleration of the host vehicle, a velocity of the host vehicle, and a yaw rate of the host vehicle.
According to one embodiment of the invention, the threat estimates are based on a first threat estimate for the host vehicle and a second threat estimate for the target vehicle.
According to one embodiment of the invention, wherein the time of collision is based on a predicted position of the target vehicle relative to the host vehicle at a predetermined period of time after the current time.
According to the present invention, there is provided a method comprising:
determining a distance offset value between the target vehicle and the host vehicle based on the determined time-to-collision, relative lateral distance, and relative longitudinal distance;
determining a threat assessment value based on the distance offset value and the distance threshold value; and
a component of the host vehicle is actuated based on the threat estimate.
According to one embodiment of the invention, the method further comprises actuating the brake when the threat assessment value exceeds a threshold value.
According to one embodiment of the invention, the method further comprises determining a lateral collision time and a longitudinal collision time.
According to one embodiment of the invention, the method further comprises determining a longitudinal distance offset value based on the lateral time-to-impact and determining a lateral distance offset value based on the longitudinal time-to-impact.
According to one embodiment of the invention, the method further comprises determining a longitudinal distance threshold based on the lateral time-to-collision and determining a lateral distance threshold based on the longitudinal time-to-collision.
According to one embodiment of the invention, the method further comprises determining a threat estimate based on the lateral time of impact and the longitudinal time of impact.
According to one embodiment of the invention, the threat estimates in the method are a number of braking threats, the number of braking threats being a measure of the change in acceleration of the host vehicle for allowing the host vehicle to stop or the target vehicle to pass the host vehicle.
According to one embodiment of the invention, the method wherein the time to collision is based on an acceleration of the host vehicle, a velocity of the host vehicle, and a yaw rate of the host vehicle.
According to one embodiment of the invention, the threat estimates are based on a first threat estimate for the host vehicle and a second threat estimate for the target vehicle.
According to one embodiment of the invention, the method wherein the time of collision is based on a predicted position of the target vehicle relative to the host vehicle at a predetermined period of time after the current time.
Drawings
FIG. 1 is a block diagram of an exemplary system for avoiding a collision between a host vehicle and a target;
FIG. 2A illustrates an exemplary intersection between a host vehicle and a target;
FIG. 2B illustrates an exemplary intersection between a host vehicle and a target;
FIG. 3 is an exemplary plot of measurements of a host vehicle measured in polar coordinates between the host vehicle and a target;
FIG. 4 is an exemplary diagram of mapping the measurements of FIG. 3 to a rectangular coordinate system;
FIG. 5 is a block diagram of an exemplary process for avoiding a collision between a host vehicle and a target.
Detailed Description
A vehicle computer may be programmed to collect data about a target, determine a distance offset value between the target and a host vehicle based on the determined time-to-collision, relative lateral distance, and relative longitudinal distance, determine a threat assessment value based on the distance offset value and a distance threshold, and actuate a component of the host vehicle based on the threat assessment value.
By determining the target's distance offset value and distance threshold, the vehicle computer may determine a threat estimate for the target for both rear-end collisions and route crossing scenarios. In addition, the vehicle computer may determine distance offset values and distance thresholds for both lateral and longitudinal directions in the vehicle coordinate system, providing additional information about the predicted trajectory of the target. Further, the vehicle computer may determine a time to collision in both the lateral and longitudinal directions, and selectively determine a distance offset value and a distance threshold value for one of the lateral and longitudinal directions based on the lateral and longitudinal time to collision. Thus, the number of calculations performed by the vehicle computer is reduced, allowing the vehicle computer to perform threat estimates on the targets more quickly.
Fig. 1 shows a system 100 for collision prevention and mitigation. Unless otherwise indicated in this disclosure, an "intersection" is defined herein as a location where a current or potential future trajectory of two or more vehicles intersects. Thus, an intersection may be any location on a surface where two or more vehicles may collide, e.g., a road, a lane, a parking lot, an entrance to a highway, a driving route, etc. Thus, an intersection, as used herein, is determined by identifying a location where two or more vehicles are likely to meet (i.e., collide), rather than a location having a predefined characteristic (e.g., two roads crossing each other) or "intersection" map label. Such a determination uses the potential future trajectories of the host vehicle 101 and other vehicles and/or other objects in the vicinity.
The computing device 105 in the host vehicle 101 is programmed to receive data 115 collected by one or more sensors 110. For example, the data 115 for the vehicle 101 may include a location of the vehicle 101, a location of a target, and the like. The location data may be in a known form, for example, geographic coordinates such as latitude and longitude coordinates obtained by a known navigation system using the Global Positioning System (GPS). For example, other examples of data 115 may include measurements of systems and components of vehicle 101, such as the speed of vehicle 101, the trajectory of vehicle 101, and so forth.
As is well known, the computing device 105 is generally programmed for communication over a network, such as the vehicle 101, including a communication (e.g., controller area network or CAN) bus. Through a network, bus, and/or other wired or wireless mechanism (e.g., a wired or wireless local area network in vehicle 101), computing device 105 may transmit messages to various devices in vehicle 101 and/or receive messages from various devices, such as controllers, actuators, sensors, etc., including sensors 110. Alternatively or additionally, where computing device 105 actually contains multiple devices, a vehicle network may be used for communication between devices that are represented in this disclosure as computing device 105. In addition, the computing device 105 may be programmed to communicate with the network 125, and as described below, the network 125 may include various wired and/or wireless network technologies, such as cellular, bluetooth, wired and/or wireless packet networks, and so forth.
The data storage 106 may be of any known type, such as a hard drive, a solid state drive, a server, or any volatile or non-volatile media. The data storage 106 may store collected data 115 sent by the sensors 110.
The sensor 110 may include various devices. For example, as is well known, various controllers in the vehicle 101 may act as sensors 110 for providing data 115 over a network or bus of the vehicle 101, the data 115 such as data 115 relating to vehicle speed, acceleration, position, subsystem and/or component status, and the like. In addition, other sensors 110 may include cameras, motion detectors, etc., i.e., sensors 110 for providing data 115 for evaluating target location, planning target path, evaluating road lane location, etc. The sensors 110 may also include short range radar, long range radar, light detection and ranging (LIDAR), and/or ultrasound transducers.
The collected data 115 may include various data collected in the vehicle 101. Examples of the collected data 115 are provided above, and further, the data 115 is generally collected using one or more sensors 110, and the data 115 may additionally include data calculated from the collected data 115 in the computing device 105 and/or at the server 130. In general, the collected data 115 may include any data that may be collected by the sensors 110 and/or calculated from such data.
When computing device 105 operates vehicle 101, vehicle 101 is an "autonomous" vehicle 101. For the purposes of this disclosure, the term "autonomous vehicle" is used to refer to vehicle 101 operating in a fully autonomous mode. The fully autonomous mode is defined as: in a fully autonomous mode, each of propulsion (typically through a powertrain including an electric motor and/or an internal combustion engine), braking, and steering of the vehicle 101 is controlled by the computing device 105 rather than by the operator. A semi-autonomous mode is one in which at least one of propulsion (typically through a powertrain including an electric motor and/or an internal combustion engine), braking, and steering of vehicle 101 is controlled, at least in part, by computing device 105 rather than by an operator.
The system 100 may also include a network 125 connected to the server 130 and the data store 135. The computer 105 may also be programmed to communicate with one or more remote sites, such as a server 130, over the network 125, for example, which may include a data store 135. Network 125 represents one or more mechanisms by which vehicle computer 105 may communicate with remote server 130. Thus, the network 125 may be one or more of a variety of wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and/or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are used). Exemplary communication networks include wireless communication networks providing data communication services (e.g., using bluetooth, IEEE802.11, vehicle-to-vehicle (V2V) such as Dedicated Short Range Communication (DSRC), etc.), local Area Networks (LANs), and/or Wide Area Networks (WANs) including the internet.
Fig. 2A and 2B illustrate an exemplary intersection including a host vehicle 101 and a target 200. In the example of fig. 2A-2B, the target 200 is shown as a target vehicle 200, and the target 200 may be an obstacle, such as a road sign, guardrail, tree, etc., with which the host vehicle 101 may collide. Host vehicle 101 may move in road lane 205 and target 200 may move in a different lane 205. In the example of fig. 2A-2B, the road has three road lanes 205a, 205B, 205c, and the road may have a different number of road lanes 205. The target 200 may move along a trajectory 210.
As shown in fig. 2A-2B, the host vehicle 101 may perform a turn 215 from the current road lane 205 and traverse the trajectory 210 of the target 200. In the example of fig. 2A, the host vehicle 101 is located in road lane 205b and the target 200 is located in road lane 205a, i.e., the host vehicle 101 is offset from the target 200 by one road lane 205. In the example of fig. 2B, host vehicle 101 is located in road lane 205c and target 200 is located in road lane 205a, i.e., host vehicle 101 is offset from target 200 by two road lanes 205. Based on the number of road lanes 205 between the host vehicle 101 and the target 200, the host vehicle 101 may take more time to complete the turn 215 and pass through the trajectory 210 of the target 200. Accordingly, the computing device 105 may determine the number of threats of a potential collision with the target 200 based on the steering 215 to be performed by the host vehicle 101.
2A-2B illustrate a route of the host vehicle 101 through the target 200, i.e., a route crossing scenario. Alternatively or additionally, the following equation may be used when the host vehicle 101 is approaching the rear of the target 200, i.e., a rear-end collision scenario. Thus, computing device 105 may determine threat estimates for target 200 under both route crossing and rear-end collision scenarios.
FIG. 3 shows data 115 collected by the sensors 110 of the host vehicle 101 and the target 200 and values determined by the computing device 105 based on the data 115. In this illustration, the data 115 includes data 115 about the trajectory of the vehicle 101, 200, e.g., as shown in FIG. 3, the trajectory of the vehicle 101, 200 is provided according to a polar coordinate system referenced to have an origin on the host vehicle 101. As described above, the target 200 moves according to the trajectory 210. The host vehicle 101 moves according to the trajectory 210. The trajectory 210 indicates where the host vehicle 101 and the target 200 will move if the host vehicle 101 and the target 200 continue to travel at their respective speeds without turning. The host vehicle 101 may define a vehicle having an origin O at a center point of a front end of the host vehicle 101 h The coordinate system of (2). The computing device 105 may use the origin O h To define the position, velocity and acceleration of the host vehicle 101 and the target 200. One or more sensors 110 may provide data 115 according to polar coordinates. In the example of fig. 3, trajectory 210 of target 200 shows that target 200 is moving toward host vehicle 101 as in, for example, a route crossing scenario. Trajectory 210 of target 200 may be shown as in, for example, a rear-end collisionThe target 200 is moving away from the host vehicle 101 in a collision scenario.
The distance R between the host vehicle 101 and the target 200 is defined as the origin O of the host vehicle 101, e.g. in meters h Origin O with target 200 t The shortest straight line in between. Origin O of target 200 t Defined as the center point of the front end of the target 200. Rate of change of distance
Is the time rate of change of distance (i.e., dR/dt), and the distance acceleration +>
Is the time rate of change of the distance rate (i.e., d) 2 R/dt 2 ). The distance R is thus the shortest absolute distance between the host vehicle 101 and the target 200. Because the host vehicle 101 is turning, the distance R may not always be aligned with the trajectory 210 of the target 200. That is, the trajectory 210 of the target 200 may define an angle (not numbered in FIG. 3) with a path defined by the distance R.
The azimuth angle θ is defined as an angle in radians defined between the trajectory 210 of the host vehicle 101 and the route defined by the distance R. Rate of change of azimuth
Is the time rate of change of the azimuth angle theta (i.e., d theta/dt), and the azimuth angle acceleration
Is azimuth change rate>
Time rate of change (i.e., d) 2 θ/dt 2 )。
When the host vehicle 101 is at the turn 215, the traveling direction of the host vehicle 101 changes. The change in direction of travel is defined as the yaw rate w in radians/second h . The computing device 105 may use the yaw rate w h To determine whether the target 200 will collide with the host vehicle 101. That is to sayIn other words, because the host vehicle 101 is turning away from the current trajectory 210, the host vehicle 101 may avoid the target 200 even if the direction of travel of the target 200 indicates a potential collision with the host vehicle 101 at a certain time.
Main speed v h Is the velocity of the host vehicle 101 in meters per second along the trajectory 210. Principal acceleration a h Is the main velocity v h Time rate of change (i.e., dv) h Dt)). Principal velocity v h And main acceleration a h Is based on the host vehicle 101 traveling along the trajectory 210. Thus, as the host vehicle 101 turns, the primary speed v h And the acceleration a of the main machine h Will vary with the trajectory 210. Target velocity v t Is the speed of the target 200 in meters per second along the trajectory 210.
FIG. 4 shows the vehicle positioned at the origin O with the host vehicle 101 h The host vehicle 101 and the target 200 in a cartesian coordinate system. A rectangular coordinate system may be used to define the orthogonal directions: the lateral direction, denoted by the variable x, and the longitudinal direction, denoted by the variable y. Instead of using polar coordinates as in fig. 3, the computing device 105 may predict the position, velocity, and acceleration of the host vehicle 101 and the target vehicle from rectangular coordinates. Specifically, the computing device 105 may determine a position, velocity, and acceleration in a longitudinal direction, and a position, velocity, and acceleration in a lateral direction, as described further below. Further, one or more sensors 110 may collect data 115 in rectangular coordinates, and computing device 105 may convert data 115 in polar coordinates to values in rectangular coordinates using distance R and azimuth angle θ.
Lateral position of target 200
Is the distance of the target 200 in the lateral direction x relative to the host vehicle 101. Target 200 lateral speed>
Is a transverse position>
In time, i.e., in combination with a number of characteristic changes>
The lateral acceleration of the target 200->
Is the transverse speed pick>
In time, i.e., in combination with a number of characteristic changes>
Longitudinal position of target 200
Is the distance in the longitudinal direction y of the target 200 relative to the host vehicle 101. The longitudinal speed of the target 200->
Is a longitudinal position->
Is changed over time, i.e., is>
The longitudinal acceleration of the target 200->
Is longitudinal speed pick>
Is based on the time rate of change, i.e.>
The values described above may be a function of time t in seconds. The computing device 105 may predict the route of the host vehicle 101 and the target 200 over a predetermined period of time T. The following equation solves for the time period T that results in the Time To Collision (TTC) between the host vehicle 101 and the target 200.
predicted relative lateral distance of target 200 with respect to host vehicle 101 at time (T + T)
Is given as:
time to longitudinal collision (TTC) long ) Is defined as the time period T when the host vehicle 101 and the target 200 reach the same longitudinal position, i.e., the relative longitudinal distance between the target 200 and the host vehicle 101
Equal to zero. Thus, at any time t, TTC long Satisfy the equation->
More specifically, TTC long Is the smallest positive real root of the following polynomial equation:
transverse TTC (TTC) lat ) Is defined as the time period T when the host vehicle 101 and the target 200 reach the same lateral position, i.e. the relative lateral distance between the target and the host vehicle
Equal to zero. Thus, at any time t, TT Clat All satisfy the equation
More specifically, TT Clat Is the smallest positive real root of the following polynomial equation:
by calculating TT Clat (t) substituting into equation (1), we find the predicted longitudinal distance offset value PredLongOff (t) at time t as:
by calculating the TTC long (t) substituting into equation (2), we find the predicted lateral distance offset value PredLatOff (t) at time t as:
wherein a is h (t)、v h (t)、w h (t)、
Is the measured data 115 of the host vehicle 101 and the target 200, and TTC as described above long (t) is the longitudinal collision time.
TTC based on lateral time to collision lat And time to longitudinal collision TTC long The computing device 105 may determine the longitudinal indication F long . Longitudinal direction being the target200 will reach the Boolean measure (Boolean measure) of the position of the host vehicle 101 in the lateral direction x or the longitudinal direction y. Namely:
when F is present long (t) =1, relative longitudinal distance between host vehicle 101 and target 200
Relative lateral distance pick-up>
Becoming zero earlier. When F is present long (t) =0, the relative lateral distance between the host vehicle 101 and target 200 +>
Relative longitudinal distance->
Becoming zero earlier.
where a > 0, b > 0, and c > 0 and are tunable parameters (i.e., values that may be, for example, root-lifted empirical verified and/or simulated changed), such as a =2.5, b =3, and c =1. In the function, a represents a predetermined maximum of the vertical and horizontal distance offset values PredLongOff, predLatOff for predictionLarge threshold, b denotes time to collision TTC for longitudinal and lateral long 、TTC lat And c represents a predetermined decay rate of the threshold function f (t). Parameter vs. longitudinal threshold a long 、b long 、c long And a lateral threshold a lat 、b lat 、c lat May be predetermined.
By mixing TTC lat (t) substituting the threshold function f (t) to obtain a longitudinal distance threshold LongDistThresh (t):
by mixing TTC long (t) substituting the threshold function f (t) to obtain a transverse distance threshold LatDistThresh (t):
the computing device 105 may determine the crash factor F based on the distance threshold, the distance offset value, and the longitudinal factor described above collision (t) of (d). Collision factor Fc ollision (t) is a Boolean measure of whether the respective distance offset value is less than a distance threshold, i.e., the collision factor F collision (t) indicates whether a collision is likely to occur at a specific time t. Crash factor F collision (t) may be defined as follows:
when indicated longitudinally by F long (t) =1, the computing device 105 determines the collision factor F based on the predicted lateral distance offset value PredLatOff (t) collision (t) of (d). When indicating in the longitudinal direction F long (t) =0, the computing device 105 determines the collision factor F based on the predicted longitudinal distance offset value PredLongOff (t) collision (t)。
The computing device 105 may determine a brake threat number BTN. The number of brake threats BTN is the host vehicle101 is used to allow the host vehicle 101 to park or the target 200 to pass a measure of the change in acceleration of the host vehicle 101. At time t, the brake threat number BTN of the host vehicle 101 h (t) can be calculated as:
wherein v is as described above h (t) host vehicle speed, TTC long (t) is the longitudinal time to collision, TTC lat (t) is the lateral collision time, F long (t) is a longitudinal indication, an
Is a user-specific parameter which specifies the maximum achievable deceleration level as a result of a braking manoeuvre of the host vehicle 101, for example, for a typical vehicle 101 traveling on a dry road>
At time t, the number of brake threats BTN of target 200 t (t) can be calculated as:
wherein v is t (t) target vehicle speed, TTC long (t) is the longitudinal time to collision, TTC lat (t) is the lateral collision time, F long (t) is a longitudinal direction indication,
Is a user-specific parameter that specifies the maximum achievable magnitude of deceleration due to a target 200 braking and/or stopping maneuver, e.g., for a typical target vehicle 200 traveling on a dry road, based on a vehicle speed change>
wherein v is h (t) and v t (t) host 101 and target 200 speeds, TTC, respectively long (t) is the longitudinal time-to-collision, latDistThresh (t) is the lateral distance threshold, predLatOff (t) is the lateral prediction offset value, and
and
is a user-specific parameter which specifies a user-specific nominal lateral speed ≦ due to a steering maneuver of the host vehicle 101 or the target 200>
And &>
Maximum achievable lateral acceleration in the mean, e.g.)>
The computing device 105 may determine an accelerated threat number ATN. The ATN is a measure for allowing one of the host vehicle 101 and the target 200 to pass a specific longitudinal acceleration of the other of the host vehicle 101 and the target 200. At time t, the number of acceleration threats, ATN, of the host vehicle 101 h (t) and target 200ATN t The accelerated threat number of (t) may be calculated as:
wherein v is h (t) and v t (t) host 101 and target 200 speeds, TTC, respectively lat (t) is the lateral time-to-collision, longdistThresh (t) is the longitudinal prediction distance threshold, predLongOff (t) is the longitudinal prediction offset value, and
and &>
Is a user-specific parameter that specifies a user-specific nominal longitudinal speed ≧ due to accelerated manipulation of the host vehicle 101 or target 200>
Or->
The maximum achievable longitudinal acceleration of the vehicle, for example,
TN(t)＝F collision (t)*min(BTN h (t)，BTN t (t)，STN h (t)，STN t (t)，ATN h (t)，ATN t (t)) (18)
Fig. 5 shows an exemplary process 500 for operating the vehicle 101 in a collision avoidance manner. The process 500 begins in block 505, where the computing device 105 actuates the sensors 110 to collect data 115 about the host vehicle 101 and the target 200 in block 505. As described above, the computing device 105 may collect data 115 regarding the position, velocity, trajectory, etc. of the target 200. Specifically, the computing device 105 may determine a distance R and an azimuth θ between the host vehicle 101 and the target 200.
Next, in block 510, the computing device 105 determines a time to longitudinal collision TTC between the host vehicle 101 and the target 200 long And time to transverse collision TTC lat . As described above, the time to longitudinal Collision TTC long The times at which the host vehicle 100 and the target 200 reach the same longitudinal position are predicted. Time to transverse collision TTC lat The times at which the host vehicle 101 and the target 200 reach the same lateral position are predicted.
Next, in block 515, the computing device 105 determines a predicted longitudinal distance offset value PredLongOff and a predicted lateral distance offset value PredLatOff. As described above, the computing device 105 converts the polar coordinates defining the position of the target 200 with respect to the host vehicle 101 into rectangular coordinates. As described above, from the rectangular coordinates, the computing device 105 can determine distance offset values in the longitudinal and lateral directions.
Next, in block 520, the computing arrangement 105 determines a longitudinal distance threshold LongDistThresh and a transverse distance threshold LatDistThresh. As described above, the longitudinal distance threshold LongDistThresh and the lateral distance threshold LongDistThresh are based on what can be used to determine the predicted relative lateral and longitudinal distances
Whether it is possible to cause a potential collision between the host vehicle 101 and the target 200.
Next, in block 525, the computing device 105 compares the predicted longitudinal offset value PredLongOff to a longitudinal distance threshold LongDistThresh and/or the predicted lateral offset value predlattofoff to a lateral distance threshold LatDistThresh. Time to longitudinal collision TTC long Less than or equal to the transverse time to collision TTC lat In time, the computing device 105 may compare the predicted lateral offset value predlattoff to the lateral distance threshold LatDistThresh to determine the collision factor F collision . Time to longitudinal collision TTC long Greater than transverse time to collision TTC lat In time, the computing device 105 may compare the predicted longitudinal offset PredLongOff to the longitudinal distance threshold LongDistThresh to determine the collision factor F collision 。
Next, in block 530, computing device 105 determines a number of threats. As described above, the number of threats is a measure of the probability of collision between the host vehicle 101 and the target 200. As described above, the threat number may be a braking threat number BTN, an acceleration threat number ATN, or a steering threat number STN. As described above, the number of threats may be based on a collision factor F collision Time to collision TTC long 、TTC lat And/or longitudinal factor F long 。
Is connected withIn block 535, computing device 105 actuates one or more components 120 based on the number of threats. For example, if the threat number is greater than 0.7, the computing device 105 may actuate the brakes to cause the host vehicle 101 to move, for example, at-6.5 meters per square meter (m/s) 2 ) Deceleration is performed. In another example, if the threat number is greater than 0.4 but less than or equal to 0.7, computing device 105 may actuate a brake, e.g., -2.0m/s 2 Of the deceleration of (c). In another example, if the threat number is greater than 0.2 but less than or equal to 0.4, the computing device 105 may display a visual alert on the HMI of the vehicle 101 and/or play an audio alert through a speaker. After block 535, the process 500 ends.
As used herein, the adverb "substantially" modifying the adjective means that the shape, structure, measurement, value, calculation, etc., may deviate from the accurately described geometry, distance, measurement, value, calculation, etc., due to deviations in materials, processing, manufacturing, sensor measurements, calculations, processing time, communication time, etc.
Computer-readable media includes any medium that participates in providing data (e.g., instructions), which may be read by a computer. Such a medium may take many forms, including but not limited to, non-volatile media, and the like. Non-volatile media includes, for example, optical or magnetic disks or other persistent memory. Volatile media include Dynamic Random Access Memory (DRAM), which typically constitutes a main memory. Conventional forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic disk, any other magnetic medium, a CD-ROM (compact disc read only drive), DVD (digital versatile disc), any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM (random access memory), a PROM (programmable read only memory), an EPROM (erasable programmable read only memory), a FLASHEEPROM (flash electrically erasable programmable read only memory), any other memory chip or cartridge, or any other medium from which a computer can read.
With respect to the media, programs, systems, methods, etc. described herein, it will be understood that while the steps of such programs, etc. have been described as occurring in a certain order, such programs may perform operations using the described steps which are performed in an order other than the order described herein. It is further understood that certain steps may be performed simultaneously, that other steps may be added, or that certain steps described herein may be omitted. For example, in process 500, one or more steps may be omitted, or steps may be performed in a different order than shown in FIG. 5. In other words, the description of the systems and/or processes herein is provided for purposes of illustrating certain embodiments and should not be construed in any way to limit the disclosed subject matter.
Accordingly, it is to be understood that the disclosure, including the above description and drawings and the following claims, is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The distances of the present invention should be determined with reference to the appended claims and/or claims included in the non-provisional patent application based thereon, as well as all distances of equivalents, rather than with reference to the above description. It is contemplated that further developments will occur in the techniques discussed herein, and that the disclosed systems and methods will be incorporated into such further embodiments. In sum, it should be understood that the disclosed subject matter is capable of modification and variation.
The article "a" or "an" modifying noun should be understood to mean one or more unless specified otherwise or the context requires otherwise. The phrase "based on" encompasses partial or complete bases.
Claims (11)
1. A method for collision mitigation and avoidance, comprising:
predicting a longitudinal distance and a lateral distance of the target vehicle relative to the host vehicle;
determining a longitudinal time to collision based on the longitudinal distance and a lateral time to collision based on the lateral distance;
determining a lateral distance offset value and a lateral distance threshold value based on the determined longitudinal time-to-collision;
determining a longitudinal distance offset value and a longitudinal distance threshold value based on the determined lateral time-to-collision;
determining a longitudinal factor based on the longitudinal time to collision and the lateral time to collision, wherein the longitudinal factor is a boolean measure of whether the target will reach the location of the host vehicle in the lateral or longitudinal direction;
determining a collision factor based on the lateral distance offset value, the longitudinal distance offset value, the lateral distance threshold value, the longitudinal distance threshold value, and the longitudinal factor, specifically: determining a collision factor based on the lateral distance offset value and the lateral distance threshold value when the longitudinal factor is expressed as the lateral collision time being greater than or equal to the longitudinal collision time; determining a collision factor based on the longitudinal distance offset value and the longitudinal distance threshold value when the longitudinal factor is expressed as a lateral collision time less than a longitudinal collision time, wherein the collision factor is a boolean measure of whether the respective distance offset value is less than the distance threshold value;
determining a number of braking threats based on a longitudinal time to collision, a lateral time to collision, and a longitudinal factor;
determining a number of turn threats based on a longitudinal time to collision, a lateral distance offset value, and a lateral distance threshold;
determining a number of acceleration threats based on a lateral collision time, a longitudinal distance offset value, and a longitudinal distance threshold;
determining a threat estimate based on the number of braking threats, the number of steering threats, and/or the number of acceleration threats, and the collision factor; and
actuating a component of the host vehicle based on the threat estimate.
2. The method of claim 1, further comprising actuating a brake when the threat assessment value exceeds a threshold.
3. The method of claim 1, wherein the threat assessment value is determined based on a braking threat number and a collision factor, the braking threat number being a measure of a change in acceleration of the host vehicle for allowing the host vehicle to park or the target vehicle to pass the host vehicle.
4. The method of claim 1, wherein the time-to-collision is determined based on an acceleration of the host vehicle, a speed of the host vehicle, and a yaw rate of the host vehicle.
5. The method of claim 1, wherein the threat assessment value is determined based on a first threat assessment value of the host vehicle and a second threat assessment value of the target vehicle.
6. The method of claim 1, wherein the time of collision is determined based on a predicted position of the target vehicle relative to the host vehicle at a predetermined period of time after a current time.
7. The method of any of claims 3-6, further comprising actuating a brake when the threat assessment value exceeds a threshold.
8. A computer programmed to perform the method of any one of claims 1-7.
9. A vehicle incorporating the computer of claim 8.
10. A computer-readable medium storing instructions executable by a computer processor to perform the method of any one of claims 1-7.
11. A system comprising a computer programmed to:
predicting a longitudinal distance and a lateral distance of the target vehicle relative to the host vehicle;
determining a longitudinal collision time based on the longitudinal distance and a lateral collision time based on the lateral distance;
determining a lateral distance offset value and a lateral distance threshold value based on the determined longitudinal time-to-collision;
determining a longitudinal distance offset value and a longitudinal distance threshold value based on the determined lateral time-to-collision;
determining a longitudinal factor based on the longitudinal time-to-collision and the lateral time-to-collision, wherein the longitudinal factor is a boolean measure of whether the target will reach the location of the host vehicle in the lateral direction or the longitudinal direction;
determining a collision factor based on the lateral distance offset value, the longitudinal distance offset value, the lateral distance threshold value, the longitudinal distance threshold value, and the longitudinal factor, specifically: determining a collision factor based on the lateral distance offset value and the lateral distance threshold value when the longitudinal factor is expressed as the lateral collision time being greater than or equal to the longitudinal collision time; determining a collision factor based on the longitudinal distance offset value and the longitudinal distance threshold value when the longitudinal factor is expressed as a lateral collision time less than a longitudinal collision time, wherein the collision factor is a boolean measure of whether the respective distance offset value is less than the distance threshold value;
determining a number of braking threats based on a longitudinal time to collision, a lateral time to collision, and a longitudinal factor;
determining a number of turn threats based on a longitudinal time to collision, a lateral distance offset value, and a lateral distance threshold;
determining a number of acceleration threats based on a lateral collision time, a longitudinal distance offset value, and a longitudinal distance threshold;
determining a threat estimate based on the number of braking threats, the number of steering threats, and/or the number of acceleration threats, and the collision factor; and
actuating a component of the host vehicle based on the threat estimate.
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| US15/409,641 | 2017-01-19 | ||
| US15/409,641 US10403145B2 (en) | 2017-01-19 | 2017-01-19 | Collison mitigation and avoidance |
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| GB2560245A (en) | 2018-09-05 |
| MX2018000603A (en) | 2018-11-09 |
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| GB201800815D0 (en) | 2018-03-07 |
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